Detection of Black Coffee Beans
Objective
The objective section will clearly state the goals of the research. It will outline what the study intends to achieve with the application of CNN and MobileNet in coffee bean classification. Objectives such as developing an accurate classification model, reducing manual labor, improving classification speed, and others will be detailed here.
Abstract
This study explores the application of deep learning techniques, particularly Convolutional Neural Networks (CNN) and MobileNet, for the classification of coffee beans. Traditional methods of coffee bean classification rely heavily on human expertise, which can be subjective and inconsistent. The integration of CNN and MobileNet promises a more accurate, consistent, and efficient approach to coffee bean classification, leveraging the strengths of these advanced deep learning models.
Keywords: coffee bean, Classification, Convolutional Neural Networks, CNN, Mobile net, Deep Learning, Artificial Intelligence.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
H/W CONFIGURATION:
Processor - I3/Intel Processor
Hard Disk - 160GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
RAM - 8GB
S/W CONFIGURATION:
Operating System : Windows 7/8/10
Server side Script : HTML, CSS, Bootstrap & JS
Programming Language : Python
Libraries : Flask, Pandas, Mysql.connector, Os, Smtplib, Numpy
IDE/Workbench : PyCharm
Technology : Python 3.6+
Server Deployment : Xampp Server
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